Source code for spinn_front_end_common.interface.profiling.profile_data
# Copyright (c) 2016 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import numpy
import math
import scipy.stats
from spinn_utilities.log import FormatAdapter
from spinn_front_end_common.data import FecDataView
logger = FormatAdapter(logging.getLogger(__name__))
# Define profiler time scale in ms
_MS_SCALE = (1.0 / 200000.0)
# Define the positions in tags
_START_TIME = 0
_DURATION = 1
[docs]class ProfileData(object):
"""
A container for profile data.
"""
START_TIME = _START_TIME
DURATION = _DURATION
__slots__ = (
# A dictionary of tag label to numpy array of start times and durations
"_tags",
# A list of tag labels indexed by the tag ID
"_tag_labels",
# The maximum time recorded
"_max_time"
)
def __init__(self, tag_labels):
"""
:param list(str) tag_labels: A list of labels indexed by tag ID
"""
self._tag_labels = tag_labels
self._tags = dict()
self._max_time = None
[docs] def add_data(self, data):
"""
Add profiling data read from the profile section.
:param bytearray data:
Data read from the profile section on the machine
"""
samples = numpy.asarray(data, dtype="uint8").view(dtype="<u4")
# Slice data to separate times, tags and flags
sample_times = samples[::2]
sample_tags_and_flags = samples[1::2]
# Further split the tags and flags word into separate arrays of tags
# and flags
sample_tags = numpy.bitwise_and(sample_tags_and_flags, 0x7FFFFFFF)
sample_flags = numpy.right_shift(sample_tags_and_flags, 31)
# Find indices of samples relating to entries and exits
sample_entry_indices = numpy.where(sample_flags == 1)
sample_exit_indices = numpy.where(sample_flags == 0)
# Convert count-down times to count up times from 1st sample
sample_times_ms = numpy.multiply(
numpy.subtract(sample_times[0], sample_times),
_MS_SCALE, dtype=float)
# Slice tags and times into entry and exits
entry_tags = sample_tags[sample_entry_indices]
entry_times_ms = sample_times_ms[sample_entry_indices]
exit_tags = sample_tags[sample_exit_indices]
exit_times_ms = sample_times_ms[sample_exit_indices]
# Loop through unique tags
for tag in numpy.unique(sample_tags):
self._add_tag_data(
entry_tags, entry_times_ms, exit_tags, exit_times_ms, tag)
def _add_tag_data(
self, entry_tags, entry_times, exit_tags, exit_times, tag):
"""
:param ~numpy.ndarray entry_tags:
:param ~numpy.ndarray entry_times:
:param ~numpy.ndarray exit_tags:
:param ~numpy.ndarray exit_times:
:param int tag:
"""
# pylint: disable=too-many-arguments
tag_label = self._tag_labels.get(tag, None)
if tag_label is None:
logger.warning("Unknown tag {} in profile data", tag)
tag_label = "UNKNOWN"
# Get indices where these tags occur
tag_entry_indices = numpy.where(entry_tags == tag)
tag_exit_indices = numpy.where(exit_tags == tag)
# Use these to get subset for this tag
tag_entry_times = entry_times[tag_entry_indices]
tag_exit_times = exit_times[tag_exit_indices]
# If the first exit is before the first
# Entry, add a dummy entry at beginning
if tag_exit_times[0] < tag_entry_times[0]:
logger.warning("Profile starts mid-tag")
tag_entry_times = numpy.append(0.0, tag_entry_times)
if len(tag_entry_times) > len(tag_exit_times):
logger.warning("profile finishes mid-tag")
tag_entry_times = tag_entry_times[
:len(tag_exit_times) - len(tag_entry_times)]
# Subtract entry times from exit times to get durations of each
# call in ms
tag_durations = numpy.subtract(tag_exit_times, tag_entry_times)
# Add entry times and durations to dictionary
self._tags[tag_label] = (tag_entry_times, tag_durations)
# Keep track of the maximum time
self._max_time = numpy.max(tag_entry_times + tag_durations)
@property
def tags(self):
"""
The tags recorded as labels.
:rtype: list(str)
"""
return self._tags.keys()
[docs] def get_mean_ms(self, tag):
"""
Get the mean time in milliseconds spent on operations with the
given tag.
:param str tag: The tag to get the mean time for
:rtype: float
"""
return numpy.average(self._tags[tag][_DURATION])
[docs] def get_n_calls(self, tag):
"""
Get the number of times the given tag was recorded.
:param str tag: The tag to get the number of calls of
:rtype: int
"""
return self._tags[tag][_DURATION].size
[docs] def get_mean_n_calls_per_ts(self, tag):
"""
Get the mean number of times the given tag was recorded per timestep.
:param str tag: The tag to get the data for
:rtype: float
"""
time_step_ms = FecDataView.get_simulation_time_step_ms()
n_points = math.ceil(
self._max_time / time_step_ms)
endpoint = n_points * time_step_ms
bins = numpy.linspace(0, endpoint, n_points + 1)
return numpy.average(numpy.histogram(
self._tags[tag][_START_TIME], bins)[0])
[docs] def get_mean_ms_per_ts(self, tag):
"""
Get the mean time in milliseconds spent on operations with the
given tag per timestep.
:param str tag: The tag to get the data for
:rtype: float
"""
time_step_ms = FecDataView.get_simulation_time_step_ms()
n_points = math.ceil(
self._max_time / time_step_ms)
endpoint = n_points * time_step_ms
bins = numpy.linspace(0, endpoint, n_points + 1)
mean_per_ts = scipy.stats.binned_statistic(
self._tags[tag][_START_TIME], self._tags[tag][_DURATION],
"mean", bins).statistic
mean_per_ts[numpy.isnan(mean_per_ts)] = 0
return numpy.average(
mean_per_ts[numpy.logical_not(numpy.isnan(mean_per_ts))])